General Crime Data

---

#df_nypd = read_csv("https://www.dropbox.com/scl/fi/kf2zk4t1onxzm2vo3lpkq/NYPD_Complaint_Data_Historic.csv?rlkey=ly36vi9v66sno80eir6rohlwn&dl=1", na = "(null)") |>
#  janitor::clean_names()

df_nypd = read_csv('data/NYPD_Complaint_Data_Historic.csv') |>
  janitor::clean_names()

Number of Cases in Each Year

df_nypd_test <- df_nypd  %>%
  janitor::clean_names() %>%
  mutate(cmplnt_fr_dt = lubridate::mdy(cmplnt_fr_dt))

df_nypd_test <- df_nypd_test %>%
  mutate(year = lubridate::year(cmplnt_fr_dt))

year_counts <- df_nypd_test %>%
  group_by(year) %>%
  summarise(count = n())

plot_ly(data = year_counts, x = ~year, y = ~count, type = 'scatter', mode = 'markers+lines', marker = list(color = viridis(6))) %>%
      layout(
        title = "Number of Cases in Each Year",
        xaxis = list(title = "Year"),
        yaxis = list(title = "Number of Cases"),
        showlegend = FALSE
      )

Number of Cases by Year, Borough, and Law Category

count_data <- df_nypd_test %>%
  group_by(boro_nm, year, law_cat_cd) %>%
  summarise(count = n()) |>
  filter(boro_nm != "(null)")

figure = ggplot(count_data, aes(x = year, y = count, color = law_cat_cd)) +
  geom_point(position = position_dodge(width = 0.8)) +
  geom_line(aes(group = law_cat_cd), position = position_dodge(width = 0.8)) +
  facet_wrap(~boro_nm, ncol = 5) +
  labs(title = "Number of Cases by Year, Borough, and Law Category",
       x = "Year",
       y = "Number of Cases",
       color = "Law Category") +
  theme_minimal() +
  theme(legend.position = "bottom",
        strip.background = element_rect(fill = "lightblue", color = "lightblue"),
        plot.margin = margin(20, 10, 20, 20, unit = "pt"),
        strip.text = element_text(size = 8),
        axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_text(margin = margin(t = 15)),
        legend.text = element_text(size = 8))

ggplotly(figure)

Top 10 Types of Crime by Borough and Year